:og:description: Abstract: In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data. We illustrate this on the Rosetta compendium [8].
:og:image:alt: Benchpress logo
:og:sitename: Benchpress causal discovery platform
:og:title: IAMB-FDR (bnlearn)
 
.. meta::
    :title: IAMB-FDR (bnlearn)
    :description: Abstract: In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data. We illustrate this on the Rosetta compendium [8].


.. _bnlearn_iambfdr: 

IAMB-FDR (bnlearn) 
*******************



.. list-table:: 

   * - Module name
     - `bnlearn_iambfdr <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/bnlearn_iambfdr>`__
   * - Package
     - `bnlearn <https://www.bnlearn.com/>`__
   * - Version
     - 4.8.3
   * - Language
     - `R <https://www.r-project.org/>`__
   * - Docs
     - `here <https://www.bnlearn.com/documentation/man/constraint.html>`__
   * - Paper
     - :footcite:t:`10.1007/978-3-540-78757-0_15`
   * - Graph type
     - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__
   * - MCMC
     - No
   * - Edge constraints
     - :ref:`Yes <edge_constraints>`
   * - Data type
     - C, D, M
   * - Data missingness
     - 
   * - Intervention type
     - 
   * - Docker 
     - `bpimages/bnlearn:4.8.3 <https://hub.docker.com/r/bpimages/bnlearn/tags>`__




IAMB-FDR 
------------


Abstract: In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data. We illustrate this on the Rosetta compendium [8].

.. rubric:: Some fields described 
* ``edgeConstraints`` Name of the JSON file containing background knowledge 


.. rubric:: Example JSON


.. code-block:: json


    [
      {
        "id": "iambfdr-zf",
        "alpha": [
          0.01,
          0.05
        ],
        "test": "zf",
        "B": null,
        "maxsx": null,
        "debug": false,
        "undirected": false,
        "timeout": null,
        "edgeConstraints": "edgeConstraints.json"
      },
      {
        "id": "iambfdr-mi",
        "alpha": [
          0.01,
          0.05
        ],
        "test": "mi",
        "B": null,
        "maxsx": null,
        "debug": false,
        "undirected": false,
        "timeout": null,
        "edgeConstraints": "edgeConstraints.json"
      }
    ]

.. footbibliography::